Product and Content Personalisation

Transcription

1 Product and Content Personalisation with IBM Digital Recommendations Lani Kakiet EMM Solutions Consultant, IBM Get Jon Adamson Multichannel Analytics Manager, JD Sports Fashion PLC IBM EMM Connected

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3 The value of personalised recommendations 55% of consumers expect retailers to use purchasing and behavioral data to offer relevant promotions 40% of consumers purchase more from retailers that deliver a personalised cross-channel shopping experience 3 *Source: Omni-channel product recommendations: Maximizing impact and customer engagement Whitepaper, February 2014.

4 Marketers are zeroing in on the opportunity 4 *Source: Omni-channel product recommendations: Maximizing impact and customer engagement Whitepaper, February 2014.

5 IBM Digital Recommendations Dynamic recommendation engine for delivering timely, personalised offers across multiple channels 5

6 Touchpoints and performance of product recommendations Home Category Product 0.6% 1.3% 7.2% 2.4% Search Results Pre Cart 1.6% Order Cart Confirmation Call Center 1.5% 0.3% 8% 15% 6

7 Product Overview 7

8 How does it work Behavioural data collection ProductView tag Shop5 tag Shop9 tag Product Carted Bought Recommendations PageView tag Element tag Content Recommendations 8

9 How does it work Algorithms View to View View To Buy Buy To Buy Abandon to Buy Algorithms are driven based on tag data. 9

10 How does it work Business rules data Category hierarchy Product attributes Eg. Price, margin, brand, dolour, ratings, image Product Recommendations PageView tag attributes Eg. Author, subject, date published, language Element tag attributes Eg. Genre, video type, video length Content Recommendations Eg. Price, margin, brand, dolour, ratings, image 10

11 How does it work Business rules Exclusion Promotion De-emphasis Exclude recommendations Force items into recommendations Move items to the end of the candidate list rather than exclude them Eg. Out of stock products, recalled products Eg. Higher margin products, new inventory Eg. Lower priced products 11

12 How does it work Individual visitor personalisation Other customers also bought: P1 P2 P3 P4 Wisdom of the crowds 12

13 How does it work Individual visitor personalisation Other customers also bought: P2 P5 P7 P9 Personalisation Products Viewed Products Carted Products Purchased Categories of Interest 13

14 How does it work Recommendation plan Targets and offer Zone header text Fallback recommendations A/B testing 14

15 How does it work Preview recommendations Reports and dashboards 15

16 Integrations with IBM Solutions 16

17 Embed image recommendations in targeted display ads and s 17

18 Deliver product recommendations in WebSphere Commerce e-spots based on customer segmentation 18

19 Combine product recommendations in real-time offers in Interact Category ID from Interact is passed when calling Product Recommendations Interact uses the products and product categories provided by Product Recsommendations to refine its real-time offer 19

20 Benefits and Customer Success 20

21 Product Recommendations benefits 1 Increased revenue 5-20% uplift in ongoing sales from targeted cross-sell and up-sell offers 2 Customer retention Personalised browsing and shopping experience for customers 3 Operational efficiency Increase productivity through automation of costly and manual processes 4 Merchandiser control Flexible business rules, affinity weightings, preview recommendations 5 Continuous optimisation Comprehensive reporting and analytics to measure, test and optimise offers 21

22 Content Recommendations benefits 1 Site attraction Bring customers to the site to boost potential sales opportunities 2 Site retention Maximise time customer is spending on site to monetise their browsing 3 Site self-service Get customer to the most relevant information as quickly as possible 22

23 Customer success stories 147% increase in cross-sell revenue 19% increase in average order value 70% increase in cross-sell revenue 8-10 hours saved each week 16% increase in online sales as influenced by recommendations 2X increase in time spent on the site 16% decrease in bounce rate when recommended pages are viewed 23

24 Customer Story JD Sports Fashion Optimising IBM Product Recommendations 24

25 Background JD Sports, Bank Fashion, Scotts Menswear and Size? all use the IBM Product Recommendations. At the early stages (before I joined), JD Sports tried to engage merchandisers by providing training and running a few AB tests but struggled to gain momentum as they had no owner to drive the project forward. All 4 fascias were below client average benchmarks Initially, slots we situated only on:- Product pages - below the fold and there were only one slot or they were tabbed behind each other The basket page extremely low on the page (below the fold ) The order confirmation page ineffective The recommendations displayed were repetitive 25

26 Initial opportunities At this stage Neelam (IBM consultant) highlighted many ways to improve the performance of our Recommendations such as:- Improve the rules and algorithms on current slots using AB testing Add a 2 nd slot to product pages (or split them from being tabbed) and ensure they are above the fold Include additional slots on key pages such as category, home, search and empty basket pages Utilise recommendation offers within s Use personalisation for returning customers in key engagement areas of the site to encourage repeat purchasing 26

27 Laying the foundations As I became more familiar with the product I realised that there were some fundamental issue that needed to be address before being able to use the product to it s full potential. Personal training I worked with Neelam to strengthen my knowledge of the tool Completely changed the Product ID used in the Product Categories report Identified essential issues with tagging and the EPCMF Requested to have full control over the slot headers Removed obsolete/complicated rules Once the above were all in place and I was confident with my level of knowledge of the product it was time to re-engage the merchandisers, so Neelam and I setup some more training were this time I was fully committed to lead the project to ensure we get the best out of the product and also improve the customer experience al the same time. 27

28 Re-engagement Training To all online merchandisers Fascia specific training sessions Open training sessions invite questions Communicate quick wins Identify long term benefits Allow merchandiser ownership/provide guidance Plan steps to improvements React to marketing events e.g. sale Continuous support Add extra attributes such as brand, price, sale, stock, offer code and even colour! 28

29 Product page improvement (February 2013) 29

30 Continuous engagement Extremely important to ensure support is continuous to keep the merchandisers engaged. Ensure regular catch up sessions with merchandisers Ensure regular calls with IBM consultancy Support experimental idea Perform AB tests Propose page re-design/recommend new slots 30

31 Current priorities recommendations Personalisation Homepage slot for repeat visitors AB Testing/optimising Re-engage with disconnected fascias Apply relevant learnings across fascias Ensure all fascias have 2 visible slots on product pages Add slots to additional pages such as Zero search/search result, empty basket and homepage 31

32 Tips for success 1. Ensure it s someone s job to improve recommendations 2. Train merchandisers to understand and use recommendations 3. Look to optimise current slots with AB testing 4. Look to add new slots on relevant pages 5. Have regular (2-4 week) catch up sessions with merchandisers 6. Ensure you set up recurring calls with an IBM consultant 7. DON T manually place products in recommendation slots! 32

33 Q&A 33

34 Thank you for your time! 34